Methods and Systems for Building Enterprise Digital Risk Prevention and Control System

Abstract

This invention discloses a method and system for constructing a digital risk prevention and control system for enterprises, relating to the field of data analysis system technology. Existing technologies lack multi-faceted risk prevention and control analysis. This invention includes a human resource risk identification unit, which analyzes and calculates the average daily production volume and average daily attendance rate on a single production line, assesses the output level of each employee, analyzes the correlation between employee seniority, and calculates turnover risk parameters; it presets production cycles and cycle production indicators, analyzes and calculates the completion rate of production indicators, obtains the minimum value among the completion rates of production indicators across all production lines as the theoretical production value, and calculates human resource risk parameters; and a warehousing risk identification unit, which obtains the quantity of outbound products in the warehouse and calculates the sales ratio, constructs an ARIMA model of outbound volume over time, and analyzes and calculates the warehousing risk index. This technical solution has advantages such as comprehensiveness, real-time performance, flexibility, accuracy, and practicality, and can significantly improve the risk prevention and control capabilities of manufacturing enterprises.

Description

Technical Field

[0001] This invention relates to the field of data analysis system technology, specifically to a method and system for constructing a digital risk prevention and control system for enterprises. Background Technology

[0002] Manufacturing enterprises are those whose core business is material processing, assembly, and production, encompassing the entire process from raw material procurement to final product delivery. These enterprises utilize mechanization, automation, or digitalization technologies to transform raw materials or semi-finished products into high-value-added goods, serving a wide range of industries such as automotive, electronics, machinery, and chemicals. Manufacturing enterprises emphasize efficient production processes, precise quality control, and supply chain collaboration to meet customer needs and market competitiveness.

[0003] For manufacturing enterprises, assessing their operational risks based on timely enterprise digital data is a powerful means of avoiding supply and demand imbalances. However, existing risk control systems can only assess risks based on single-aspect data and lack a multi-faceted risk control system and its construction methods. They cannot simultaneously assess human resources and warehousing risks for manufacturing enterprises, resulting in poor risk avoidance effects for manufacturing enterprises. Summary of the Invention

[0004] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a method and system for constructing a digital risk prevention and control system for enterprises, which can effectively solve the problem of lack of multi-angle risk prevention and control analysis in the existing technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] This invention provides a method and system for constructing a digital risk prevention and control system for enterprises, comprising at least:

[0007] The human resource risk identification unit analyzes and calculates the average daily production volume and average daily attendance rate on a single production line, divides the average daily production volume by the average daily attendance rate to obtain the output evaluation value, and evaluates the output level of each employee.

[0008] Analyze the correlation between employee output level and employee length of service, and calculate the turnover risk parameter based on the proportion of employees at each output level, turnover rate and number of new hires, and select a calculation formula according to the correlation of length of service.

[0009] The system has a pre-set production cycle and cycle production targets. The production target completion rate is calculated by combining the average daily production volume of the production line and the turnover risk parameters. The minimum value of the production target completion rate of all production lines is recorded as the theoretical production value. Human resource risk parameters are calculated based on the theoretical production value.

[0010] The warehouse risk identification unit obtains the number of outbound products in the warehouse and calculates the sales ratio, calculates the inbound ratio based on the theoretical daily output value, and constructs an ARIMA model of outbound volume with respect to time based on time series analysis.

[0011] When the sales ratio is greater than the inbound ratio, the holding reference value is calculated based on the difference between the sales ratio and the inbound ratio. The total outbound volume is calculated using the ARIMA model. The warehousing risk index is then calculated by combining the holding reference value, the total outbound volume, and the total quantity in the warehouse.

[0012] When the sales ratio is less than the inventory inflow ratio, the full inventory reference value is calculated based on the difference between the sales ratio and the inventory inflow ratio and the preset full inventory coefficient. The warehouse risk index is then calculated by combining the total outflow volume and the total quantity in the warehouse.

[0013] This technical solution comprehensively considers the risks faced by manufacturing enterprises in multiple aspects, including human resources and warehousing, avoiding the one-sidedness of risk assessment from a single perspective, thereby improving the comprehensiveness and accuracy of risk assessment. Based on enterprise digital data, it can assess the risks of manufacturing enterprises in real time, helping them to promptly identify and respond to potential risks and improve the timeliness of risk prevention and control. The risk identification unit can flexibly select calculation formulas and assessment methods according to different risk indicators and actual situations, thereby improving the flexibility and applicability of risk assessment. By constructing statistical methods such as the ARIMA model, it can more accurately predict and analyze key data such as the enterprise's outbound volume and production target completion rate, providing a more precise basis for risk assessment. This technical solution is easy to implement and operate, applicable to various types of manufacturing enterprises, and helps enterprises improve their risk management level and enhance their market competitiveness. This technical solution has the advantages of comprehensiveness, real-time performance, flexibility, accuracy, and practicality, which can significantly improve the risk prevention and control capabilities of manufacturing enterprises and provide strong support for their sustainable development.

[0014] Furthermore, the calculation process for average daily production is as follows:

[0015] Employees on the same production line were labeled as follows: Where i represents the workstation number of each employee, i = 1, 2, 3, ..., j, and j is the total number of employees on the production line. The current daily, weekly, and monthly processing volumes of the production line are denoted as follows: The valid working days within the most recent week and the most recent month are recorded as follows: Substitute into the formula The calculations were performed to obtain the average daily production volume. .

[0016] Furthermore, the calculation process for the average daily attendance rate is as follows:

[0017] Record the number of products processed by each employee each day as the daily output. Obtain multiple daily outputs and calculate the average to obtain a reference output value for each employee. Let the reference output value for any employee be... Let the employee's daily output be... Substitute into the formula The calculation is performed in the following way: Using a preset ratio, the employee's daily attendance index is obtained. ;

[0018] The true attendance rate is obtained by summing the daily attendance index of each employee, and the average of multiple true attendance rates is calculated to obtain the daily average attendance rate.

[0019] Furthermore, the employee output rating assessment process is as follows:

[0020] Get the maximum value from the employee output reference values. and minimum value Substitute into the formula The range span is calculated in the middle. The output assessment value is recorded as ;

[0021] Substituting into the formula, the primary interval is calculated. Intermediate range Advanced range These correspond to production levels one, two, and three, respectively.

[0022] Furthermore, the calculation process for the turnover risk parameters is as follows:

[0023] The percentage of employees at different output levels on the same production line is denoted as follows: A, B, and C correspond to employees with output levels of 1, 2, and 3, respectively. An evaluation period is set, and the employee turnover rate of the production line within the evaluation period is obtained. The percentage of employees at each output level among the departing employees is recorded as follows: , , ;

[0024] When the correlation coefficient of seniority is less than the preset correlation threshold, substitute it into the formula. Calculations are performed to obtain the turnover risk parameters. ;

[0025] When the correlation coefficient of seniority is greater than or equal to the preset correlation threshold, it is substituted into the formula. Calculations are performed to obtain the turnover risk parameters. ;

[0026] in The preset weighting coefficients, This indicates the number of employees recruited during the evaluation period.

[0027] Furthermore, the calculation process for human resource risk parameters is as follows:

[0028] The system has a pre-set production cycle and cycle production targets to obtain the average daily production volume of each production line. and turnover risk parameters Substitute into the formula The calculation yields the production target completion rate for each production line. ,in These are preset constant coefficients. These represent the production cycle and the evaluation cycle, respectively. Indicates taking values ​​not less than The smallest integer;

[0029] The theoretical production value is the minimum of the production target completion rates of all production lines. Substitute into the formula Calculations are performed to obtain human resource risk parameters. where e is the natural constant. These are periodic production indicators.

[0030] Furthermore, the process of calculating the warehousing risk index based on the sales ratio and the warehousing inflow ratio is as follows:

[0031] Calculating the future based on the ARIMA model Total outbound volume within the day ;

[0032] When the sales ratio is greater than the inventory intake ratio, substitute into the formula. Calculations are performed to obtain a reference value for the holdings. ,in Indicates not less than Substitute the smallest integer into the formula The storage risk index is calculated in the following way. ;

[0033] When the sales ratio is less than the inventory intake ratio, substitute into the formula. Calculations are performed to obtain a full position reference value. ,in Indicates not less than Substitute the smallest integer into the formula The storage risk index is calculated in the following way. ;

[0034] in This indicates the total quantity of products currently in storage. This is the preset full position coefficient.

[0035] Furthermore, it also includes a comprehensive assessment unit to obtain warehousing risk index and human resource risk parameters, normalize them, and substitute them into the formula. The structured risk index is obtained through calculation. ,in The preset weighting coefficients, and All are integers and satisfy .

[0036] Furthermore, it also includes an outbound risk control unit, which constructs an outbound product system composed of different batches of products, and optimizes the proportion of different batches of products in the outbound product system based on quality feedback data of different batches of products, including the following steps:

[0037] S1: Denote the currently analyzed product as the target product, and obtain the production interval between two adjacent batches of the target product. Obtain the warranty period of the target product ,in Indicates the warranty period of the target product;

[0038] S2: According to production interval Warranty period Divided into multiple unit periods, represented as , , ..., , where k is a positive integer and The unit period numbers are 1, 2, ..., k+1, and each unit period is bound to a data set. , where n represents the unit periodicity of the set;

[0039] S3: Record products with quality problems as problematic products, conduct after-sales inspections on problematic products and mark them as either quality problems or human-caused problems. When a problematic product is marked as a quality problem, obtain the sales time and after-sales time of the problematic product and calculate the time difference. Based on the time difference between the sales time and the after-sales time, divide it into the corresponding data set.

[0040] S4: Obtain the total number of elements in different data sets and substitute them into the formula. The interval distribution coefficient is calculated using the following method:

[0041] Indicates the interval distribution coefficient;

[0042] Represents a data set Total amount of internal elements;

[0043] These represent the right and left endpoints of the unit period with index n, respectively.

[0044] S5: Obtain any batch of products and label it as a controlled product. Obtain the sales volume and shelf-listing time of the controlled product. Calculate the time difference between the shelf-listing time and the current time and label it as the on-sale duration. Obtain the sequence number of the unit period to which the on-sale duration belongs and label it as q. Obtain the number of problematic products in the controlled products and divide it by the sales volume of the controlled products and label it as the defect rate. Substitute into the formula The residual risk value is obtained through calculation. ,in These represent the market share of the regulated products;

[0045] S6: Preset a defect risk threshold, compare the defect risk value of the controlled product with the defect risk threshold, and adjust the proportion of controlled products in the outbound product system.

[0046] The methodology for building an enterprise digital risk prevention and control system, applied to the aforementioned enterprise digital risk prevention and control system, includes the following steps:

[0047] Step 1: Establish multiple production lines based on the production process, calculate the average daily production volume and average daily attendance rate of each production line, divide the average daily production volume by the average daily attendance rate to calculate the output evaluation value, and evaluate the output level of each employee based on the output evaluation value.

[0048] Step 2: Obtain the output level and length of service of different employees to calculate the length of service correlation. There is a preset correlation threshold. When the length of service correlation is less than the correlation threshold, the turnover risk parameters of each production line are calculated based on the employee turnover rate, the number of recruits and the proportion of employees of each output level. When the length of service correlation is greater than or equal to the correlation threshold, the turnover risk parameters are further calculated by combining the proportion of employees of each output level among the departing employees.

[0049] Step 3: Calculate the production target completion rate of each production line based on the turnover risk parameters, obtain the minimum value of the production target completion rate of all production lines as the theoretical production value, analyze and calculate the human resource risk parameters, and optimize human resources for the production line corresponding to the theoretical production value when the human resource risk parameters are greater than the preset human resource risk threshold.

[0050] Step 4: Assess product warehousing risks based on product sales and production data, analyze and calculate the warehousing risk index, and adjust the production line output when the warehousing risk index is greater than the preset warehousing risk threshold. Combine human risk parameters to calculate the enterprise's structured risk index.

[0051] Step 5: Construct an outbound product system consisting of different batches of products, adjust the outbound quantity of each batch of products according to the outbound product system, and optimize the proportion of different batches of products in the outbound product system based on the quality feedback data of different batches of products.

[0052] The technical solution provided by this invention has the following advantages compared with the known prior art:

[0053] 1. This invention calculates human resource risk parameters by comprehensively considering turnover rate, number of new hires, and the correlation of seniority of employees on different production lines. This helps managers understand whether there is a risk of human resource loss affecting the production indicators of each production line. When the human resource risk parameter is larger, it indicates that one or more production lines have potential human resource loss risks, which leads to a decrease in the production efficiency of the entire production process. This reminds managers to make timely adjustments to reduce the impact of human resource loss on production efficiency.

[0054] 2. This invention predicts future outbound volume based on time series analysis and further analyzes and calculates the inbound ratio based on the theoretical production value calculated by the human risk identification unit. It compares the sales ratio and the inbound ratio and selects different methods to calculate the warehousing risk index under different conditions to reflect whether the warehousing level is in a good and stable state or whether there is an over-storage problem. This makes it easier for staff to control product output and optimize and adjust the warehousing capacity.

[0055] 3. The defect risk value obtained by this invention is calculated by combining the interval distribution coefficient with the defect rate and market share of the controlled product. It will increase as the quality of the controlled product decreases and the market share increases, thus reflecting the degree of impact of a certain batch of target products on the product quality reputation of the enterprise. When the defect risk value is larger, it indicates that there are obvious quality problems in the batch of products and the impact is wide-ranging. It is necessary to optimize the market share in a timely manner, which is conducive to staff reducing the risk impact of each batch of products. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0057] Figure 1 This is an overall module block diagram of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0059] The present invention will be further described below with reference to embodiments.

[0060] Example 1:

[0061] See Figure 1 The construction of an enterprise's digital risk prevention and control system should include at least the following:

[0062] The human resource risk identification unit breaks down the production process into multiple production steps, each corresponding to a production line. Each production line is staffed with multiple employees to perform the production work for that step. Analysis is conducted for each employee, and the analysis process is as follows:

[0063] Employees on the same production line were labeled as follows: Where i represents the workstation number of each employee, i=1,2,3,…,j, and j is the total number of employees on the production line. The current daily, weekly, and monthly processing volumes of the production line (i.e., the number of products processed on the most recent full workday, the most recent week, and the most recent month) are recorded as follows: The valid working days within the most recent week and the most recent month are recorded as follows: (Effective working days equal the total duration of the cycle minus holidays within the cycle), substitute into the formula The calculations were performed to obtain the average daily production volume. ;

[0064] Record the number of products processed by each employee each day as the daily output (i.e., the number of products produced on the production line in one day). Obtain multiple daily outputs and calculate the average to obtain a reference output value for each employee. Attendance is evaluated based on each employee's reference output value.

[0065] Let the output reference value of any employee be... Let the employee's daily output be... Substitute into the formula The calculation is performed in the following way: Using a preset ratio, the employee's daily attendance index is obtained. (The daily attendance index reflects the ratio of an employee's actual output to their predetermined output reference value, thereby assessing the employee's actual output on that day. It is applicable to situations where employees leave their posts or take leave midway through the day and helps to accurately assess the daily attendance rate.)

[0066] Calculate the sum of the daily attendance index for each employee to obtain the daily true attendance rate. Obtain multiple true attendance rates (the true attendance rates for each day in the most recent month) and calculate the average daily attendance rate.

[0067] The output assessment value is calculated by dividing the average daily production by the average daily attendance rate. Based on this output assessment value, each employee's output level is evaluated.

[0068] Get the maximum value among the reference values ​​for each employee's output. and minimum value Substitute into the formula The range span is calculated in the middle. The output assessment value is recorded as Substituting into the formula, the primary interval is calculated. Intermediate range Advanced range These correspond to production levels one, two, and three, respectively.

[0069] Obtain the range to which each employee's output level belongs, and label them as junior, intermediate, or senior employees (for example, if an employee's output level belongs to the junior range, label them as a junior employee).

[0070] It should be noted that by assessing and labeling the output levels of each employee, managers can analyze the output capabilities of each employee based on data. The higher an employee's output level, the greater the benefits that employee can bring to the company's production during the work process. Correspondingly, the higher the level of the employee, the greater the value to the company. The loss of high-value employees will affect the company's human resources and cause the risk of human resource loss.

[0071] Furthermore, a turnover risk analysis was conducted for each production line, including:

[0072] The percentage of employees at different output levels on the same production line is denoted as follows: A, B, and C correspond to employees with output levels of 1, 2, and 3, respectively.

[0073] The average length of service for employees at different output levels is calculated and recorded as follows: Substitute into the formula The calculation is performed to obtain the work experience correlation. ;

[0074] It should be noted that the seniority correlation reflects the degree to which the product output efficiency on the production line is correlated with the employees' seniority. When the seniority correlation is below a certain threshold, it indicates that the correlation between the employees' output efficiency and seniority on the production line is low. When there is an increase in turnover and loss of human resources on the production line, it can be effectively compensated for by recruiting and training new employees. Conversely, when the seniority correlation is greater than or equal to a certain threshold, it indicates that there is a positive correlation between the employees' output efficiency and seniority on the production line. Accordingly, when the turnover rate on the production line increases, the output efficiency of the entire production line will decrease significantly, and this decrease is difficult to compensate for by training new employees, resulting in a higher risk of loss of human resources.

[0075] When the seniority correlation is less than a preset correlation threshold (in one specific embodiment, the value is 1), an evaluation period is set (in one specific embodiment, the evaluation period is one quarter), and the employee turnover rate of the production line within the evaluation period is obtained. (The turnover rate equals the number of employees who left during the evaluation period divided by the average number of employees during the evaluation period), substituting into the formula. Calculations are performed to obtain the turnover risk parameters. ,in The preset weighting coefficients (in one specific embodiment, The values ​​are 1, 2, and 3. This indicates the number of employees recruited during the evaluation period.

[0076] When the seniority correlation is greater than or equal to a preset correlation threshold (in one specific embodiment, the value is 1), the total number of departing employees within the evaluation period is obtained, and the proportion of employees at each output level among the departing employees is calculated and denoted as follows: , , Substitute the values ​​into the formula to calculate the turnover risk parameters. The formula is as follows ,in The preset weighting coefficients (in one specific embodiment, The value is 1).

[0077] It should be noted that the turnover risk parameter reflects the impact of employee turnover on the production efficiency of different production lines. The higher the turnover risk parameter, the more likely it is that there is a loss of high-output personnel on that production line. If managers do not take corresponding human resource management measures, the production efficiency of that production line will continue to decline and will be difficult to compensate for by recruiting and training new employees, resulting in irreparable human resource losses.

[0078] Furthermore, conduct a human resource risk analysis of the production process:

[0079] The system has a pre-defined production cycle and cycle production targets (i.e., the production targets that the company needs to achieve within the production cycle). It obtains the average daily production volume of each production line and turnover risk parameters, and substitutes them into the formula. The calculation yields the production target completion rate for each production line. ,in This is a preset constant coefficient (in one specific embodiment, the value is 0.5). These represent the production cycle and the evaluation cycle, respectively. Indicates taking values ​​not less than The smallest integer;

[0080] The theoretical production value is the minimum of the production target completion rates of all production lines. Substitute into the formula Calculations are performed to obtain human resource risk parameters. where e is the natural constant. These are periodic production indicators.

[0081] It should be noted that calculating human resource risk parameters can help managers understand whether there is a risk of human resource loss affecting the production indicators of each production line. When the human resource risk parameter is higher, it means that one or more production lines have potential human resource loss risks (i.e., increased turnover rate, and difficulty in balancing by recruiting employees), which leads to a decrease in the production efficiency of the entire production process (usually the production efficiency of each production line affects each other, and when the production efficiency of one line decreases, it will cause the efficiency of the entire production process to decrease). This reminds managers to make timely adjustments to reduce the impact of human resource loss on production efficiency.

[0082] The warehousing risk identification unit assesses product warehousing risks based on product sales and production data, including:

[0083] Obtain the theoretical production value and divide it by the production cycle to get the theoretical daily output value;

[0084] Unsold products stored in the warehouse are recorded as stored products, and sold products are recorded as outgoing products. The daily outgoing product quantity is calculated by dividing the stored product quantity by the outgoing product quantity to obtain the sales ratio. The inbound ratio is obtained by dividing the theoretical daily output value by the quantity of stored products. Set an analysis period, obtain the number of products shipped out each day in the most recent analysis period as the daily shipment volume, perform time series analysis on the daily shipment volume, and construct an ARIMA model of the daily shipment volume with respect to time.

[0085] It should be noted that time series analysis is a conventional data processing method in existing technologies. Its main steps include data preparation, model selection, model evaluation, and prediction. It can provide a specific model based on the relationship between data and time, and then use the model to predict future data.

[0086] When the sales ratio is greater than the inventory intake ratio, substitute into the formula. Calculations are performed to obtain a reference value for the holdings. ,in Indicates not less than The smallest integer, calculated based on the ARIMA model for the future. Total outbound volume within the day Substitute into the formula The storage risk index is calculated in the following way. ,in This indicates the total quantity of products currently in storage;

[0087] It should be noted that when the sales ratio is greater than the inventory inflow ratio, the warehousing risk index reflects the ratio between the sales difference and the inventory in the warehouse over a certain period of time. This relationship reflects whether the warehousing level is in a good and stable state, that is, whether the quantity of warehoused products will increase or decrease due to market fluctuations. Correspondingly, the warehousing risk will increase with the change in the quantity of warehoused products relative to the benchmark warehouse quantity (in this case, there may be too much or too little inventory, both of which are not conducive to warehousing stability).

[0088] When the sales ratio is less than the inventory intake ratio, a full inventory coefficient is preset. (Depending on the warehouse's maximum capacity, in one specific embodiment, the value is 1), substituting into the formula Calculations are performed to obtain a full position reference value. ,in Indicates not less than The smallest integer, calculated based on the ARIMA model for the future. Total outbound volume within the day Substitute into the formula The storage risk index is calculated in the following way. .

[0089] It should be noted that when the sales ratio is less than the inventory inflow ratio, the warehousing risk index reflects the ratio between the sales difference and the total warehousing capacity within a certain period in the future. This relationship reflects whether there is an over-storage problem in the warehousing level, that is, whether the quantity of warehoused products will exceed the warehouse capacity limit due to product backlog. Correspondingly, the warehousing risk will increase as the quantity of warehoused products exceeds the normal storage capacity (i.e., the general inventory quantity in the warehouse) (in this case, there may be a risk that the inventory exceeds the warehouse storage limit).

[0090] The comprehensive assessment unit calculates the enterprise's structured risk index based on the warehousing risk index and human resource risk parameters, where:

[0091] Obtain the warehousing risk index and human resource risk parameters, normalize them, and substitute them into the formula. The structured risk index is obtained through calculation. ,in The preset weighting coefficients, and All are integers and satisfy .

[0092] Example 2:

[0093] Reference Figure 1 Based on the enterprise digital risk prevention and control system construction system in Example 1, in actual application, warehouse inventory and shipment are carried out according to the order of batches. This results in the market share of products being mainly dominated by a certain batch of products. When a large number of quality problems occur in that batch of products, the return and exchange rate of products on sale will rise rapidly, thereby affecting the company's reputation and causing the risk of unsold goods. Therefore, it is necessary to optimize the product shipment system.

[0094] Compared to Embodiment 1, this embodiment differs in that it further includes:

[0095] The outbound risk control unit constructs an outbound product system composed of different batches of products. Based on quality feedback data from different batches, it optimizes the proportion of different batches of products in the outbound product system, including the following steps:

[0096] S1: Denote the currently analyzed product as the target product, and obtain the production interval between two adjacent batches of the target product. (Generally, a fixed periodic interval is used; when multiple production intervals of different lengths exist, their average value is taken.) Obtain the warranty period for the target product. ,in Indicates the warranty period of the target product;

[0097] S2: According to production interval Warranty period Divided into multiple unit periods, represented as , , ..., , where k is a positive integer and The unit period numbers are 1, 2, ..., k+1, and each unit period is bound to a data set. , where n represents the unit periodicity of the set;

[0098] S3: Record products with quality problems as problematic products, conduct after-sales inspection on problematic products and mark them as either quality problems or human-caused problems (after-sales inspection is an existing technology used to determine whether the problem of a problematic product is caused by human factors or by the product's own quality). When a problematic product is marked as a quality problem, obtain the product's sales time and after-sales time (i.e., the time when the problematic product enters the after-sales service process) and calculate the time difference. Based on the time difference between the sales time and the after-sales time, divide it into the corresponding data set.

[0099] S4: Obtain the total number of elements in different data sets and substitute them into the formula. The interval distribution coefficient is calculated using the following method:

[0100] Indicates the interval distribution coefficient;

[0101] Represents a data set Total amount of internal elements;

[0102] These represent the right and left endpoints of the unit period with index n, respectively.

[0103] It should be noted that the interval distribution coefficients corresponding to different datasets reflect the distribution relationship between the appearance of quality problems of the target product and the time period. When the interval distribution coefficient is larger, it indicates that the quality problems of the target product will be concentrated in that time period.

[0104] S5: Obtain any batch of products and label it as a controlled product. Obtain the sales volume and shelf-listing time of the controlled product. Calculate the time difference between the shelf-listing time and the current time and label it as the on-sale duration. Obtain the sequence number of the unit period to which the on-sale duration belongs and label it as q. Obtain the number of problematic products in the controlled products and divide it by the sales volume of the controlled products and label it as the defect rate. Substitute into the formula The residual risk value is obtained through calculation. ,in These represent the market share of the regulated products (i.e., the proportion of regulated products among all target products on sale).

[0105] S6: Preset a defect risk threshold. When the defect risk value of the controlled product is greater than the defect risk threshold, reduce the proportion of the controlled product in the outbound product system and conduct quality inspection on the controlled product in the warehouse. When the defect risk value of the controlled product is less than the defect risk threshold, increase the proportion of the controlled product in the outbound product system. When the defect risk value of the controlled product is equal to the defect risk threshold, maintain the proportion of the controlled product in the outbound product system.

[0106] It should be noted that the defect risk value, which is calculated by combining the interval distribution coefficient with the defect rate and market share of the regulated product, will increase as the quality of the regulated product decreases (defect rate increases) and the market share increases (wider impact). This reflects the degree of impact of a certain batch of target products on the company's product quality reputation. When the defect risk value is higher, it indicates that the batch of products has obvious quality problems and a wide impact, and it is necessary to optimize the market share in a timely manner to reduce the risk impact of the batch of products.

[0107] The proposed method for constructing an enterprise digital risk prevention and control system, based on the enterprise digital risk prevention and control system construction system in Embodiments 1 and 2 above, includes the following steps:

[0108] Step 1: Establish multiple production lines based on the production process. Calculate the average daily production volume of each production line based on the daily, weekly, and monthly processing volumes. Obtain the average daily attendance rate of employees on the same production line. Divide the average daily production volume by the average daily attendance rate to calculate the output evaluation value. Evaluate the output level of each employee based on the output evaluation value.

[0109] Step 2: Obtain the output level and length of service of different employees to calculate the length of service correlation. There is a preset correlation threshold. When the length of service correlation is less than the correlation threshold, the turnover risk parameters of each production line are calculated based on the employee turnover rate, the number of recruits and the proportion of employees of each output level. When the length of service correlation is greater than or equal to the correlation threshold, the turnover risk parameters are further calculated by combining the proportion of employees of each output level among the departing employees.

[0110] Step 3: Calculate the production target completion rate of each production line based on the turnover risk parameters, obtain the minimum value of the production target completion rate of all production lines as the theoretical production value, analyze and calculate the human resource risk parameters, and optimize human resources for the production line corresponding to the theoretical production value when the human resource risk parameters are greater than the preset human resource risk threshold.

[0111] Step 4: Assess product warehousing risks based on product sales and production data, analyze and calculate the warehousing risk index, and adjust the production line output when the warehousing risk index is greater than the preset warehousing risk threshold. Combine human risk parameters to calculate the enterprise's structured risk index.

[0112] Step 5: Construct an outbound product system consisting of different batches of products, adjust the outbound quantity of each batch of products according to the outbound product system, and optimize the proportion of different batches of products in the outbound product system based on the quality feedback data of different batches of products.

[0113] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A system for constructing an enterprise digital risk prevention and control system, characterized by... include: The human resource risk identification unit analyzes and calculates the average daily production volume and average daily attendance rate on a single production line. It divides the average daily production volume by the average daily attendance rate to obtain an output assessment value and evaluates the output level of each employee. It analyzes the correlation between employee output level and length of service, and calculates turnover risk parameters based on the employee percentage at each output level, turnover rate, and number of new hires, using a calculation formula selected according to the length of service correlation. With preset production cycles and cycle production indicators, it analyzes and calculates the completion rate of production indicators by combining the average daily production volume of the production line and the turnover risk parameters. It obtains the minimum value among the completion rates of production indicators for all production lines and records it as the theoretical production value, then calculates human resource risk parameters based on the theoretical production value. The warehouse risk identification unit acquires the number of outbound products in the warehouse and calculates the sales ratio. It calculates the inbound ratio based on the theoretical daily output value and constructs an ARIMA model of outbound volume over time based on time series analysis. When the sales ratio is greater than the inbound ratio, it calculates the holding reference value based on the difference between the sales ratio and the inbound ratio. It calculates the total outbound volume through the ARIMA model and combines the holding reference value, the total outbound volume, and the total quantity in the warehouse to calculate the warehouse risk index. When the sales ratio is less than the inventory inflow ratio, the full inventory reference value is calculated based on the difference between the sales ratio and the inventory inflow ratio and the preset full inventory coefficient. The warehouse risk index is then calculated by combining the total outflow volume and the total quantity in the warehouse. It also includes an outbound risk control unit, which constructs an outbound product system composed of different batches of products, and optimizes the proportion of different batches of products in the outbound product system based on quality feedback data of different batches of products, including the following steps: S1: Denote the currently analyzed product as the target product, and obtain the production interval between two adjacent batches of the target product. Obtain the warranty period of the target product ,in Indicates the warranty period of the target product; S2: According to production interval Warranty period Divided into multiple unit periods, represented as , , ..., where k is a positive integer and The unit period numbers are 1, 2, ..., k+1, and each unit period is bound to a data set. , where n represents the unit periodicity of the set; S3: Record products with quality problems as problematic products, conduct after-sales inspections on problematic products and mark them as either quality problems or human-caused problems. When a problematic product is marked as a quality problem, obtain the sales time and after-sales time of the problematic product and calculate the time difference. Based on the time difference between the sales time and the after-sales time, divide it into the corresponding data set. S4: Obtain the total number of elements in different data sets and substitute them into the formula. The interval distribution coefficient is calculated using the following method: Indicates the interval distribution coefficient; Represents a data set Total amount of internal elements; These represent the right and left endpoints of the unit period with index n, respectively. S5: Obtain any batch of products and label it as a controlled product. Obtain the sales volume and shelf-listing time of the controlled product. Calculate the time difference between the shelf-listing time and the current time and label it as the on-sale duration. Obtain the sequence number of the unit period to which the on-sale duration belongs and label it as q. Obtain the number of problematic products in the controlled products and divide it by the sales volume of the controlled products and label it as the defect rate. Substitute into the formula The residual risk value is obtained through calculation. ,in These represent the market share of the regulated products; S6: Preset a defect risk threshold, compare the defect risk value of the controlled product with the defect risk threshold, and adjust the proportion of controlled products in the outbound product system; The comprehensive assessment unit obtains the warehousing risk index and human resource risk parameters, normalizes them, and substitutes them into the formula. The structured risk index is obtained through calculation. ,in The preset weighting coefficients, and All are integers and satisfy ; For human resource risk parameters, This is a warehousing risk index.

2. The enterprise digital risk prevention and control system construction system according to claim 1, characterized in that, The calculation process for average daily production is as follows: Employees on the same production line were labeled as follows: Where i represents the workstation number of each employee, i = 1, 2, 3, ..., j, and j is the total number of employees on the production line. The current daily, weekly, and monthly processing volumes of the production line are denoted as follows: The valid working days within the most recent week and the most recent month are recorded as follows: Substitute into the formula The calculations were performed to obtain the average daily production volume. .

3. The enterprise digital risk prevention and control system construction system according to claim 2, characterized in that, The calculation process for average daily attendance rate is as follows: Record the number of products processed by each employee each day as the daily output. Obtain multiple daily outputs and calculate the average to obtain a reference output value for each employee. Let the reference output value for any employee be... Let the employee's daily output be... Substitute into the formula The calculation is performed in the following way: Using a preset ratio, the employee's daily attendance index is obtained. ; The true attendance rate is obtained by summing the daily attendance index of each employee, and the average of multiple true attendance rates is calculated to obtain the daily average attendance rate.

4. The enterprise digital risk prevention and control system construction system according to claim 3, characterized in that, The employee output rating assessment process is as follows: Get the maximum value from the employee output reference values. and minimum value Substitute into the formula The range span is calculated in the middle. The output assessment value is recorded as ; Substituting into the formula, the primary interval is calculated. Intermediate range Advanced range These correspond to production levels one, two, and three, respectively.

5. The enterprise digital risk prevention and control system construction system according to claim 2, characterized in that, The calculation process for turnover risk parameters is as follows: The percentage of employees at different output levels on the same production line is denoted as follows: A, B, and C correspond to employees with output levels of 1, 2, and 3, respectively. An evaluation period is set, and the employee turnover rate of the production line within the evaluation period is obtained. The percentage of employees at each output level among the departing employees is recorded as follows: , , ; When the correlation coefficient of seniority is less than the preset correlation threshold, substitute it into the formula. Calculations are performed to obtain the turnover risk parameters. ; When the correlation coefficient of seniority is greater than or equal to the preset correlation threshold, it is substituted into the formula. Calculations are performed to obtain the turnover risk parameters. ; in The preset weighting coefficients, This indicates the number of employees recruited during the evaluation period.

6. The enterprise digital risk prevention and control system construction system according to claim 1, characterized in that, The calculation process for human resource risk parameters is as follows: The system has a pre-set production cycle and cycle production targets to obtain the average daily production volume of each production line. and turnover risk parameters Substitute into the formula The calculation yields the production target completion rate for each production line. ,in These are preset constant coefficients. These represent the production cycle and the evaluation cycle, respectively. Indicates taking values ​​not less than The smallest integer; The theoretical production value is the minimum of the production target completion rates of all production lines. Substitute into the formula Calculations are performed to obtain human resource risk parameters. where e is the natural constant. These are periodic production indicators.

7. The enterprise digital risk prevention and control system construction system according to claim 6, characterized in that, The process of calculating the warehousing risk index based on the sales ratio and the inventory intake ratio is as follows: Calculating the future based on the ARIMA model Total outbound volume within the day ; When the sales ratio Greater than the inbound ratio When, substitute into the formula Calculations are performed to obtain a reference value for the holdings. ,in Indicates not less than Substitute the smallest integer into the formula The storage risk index is calculated in the following way. ; When the sales ratio is less than the inventory intake ratio, substitute into the formula. Calculations are performed to obtain a full position reference value. ,in Indicates not less than Substitute the smallest integer into the formula The warehouse risk index was calculated. ; in This indicates the total quantity of products currently in storage. This is the preset full position coefficient.

8. A method for constructing an enterprise digital risk prevention and control system, applied to the enterprise digital risk prevention and control system construction system as described in any one of claims 1-7, characterized in that, Includes the following steps: Step 1: Establish multiple production lines based on the production process, calculate the average daily production volume and average daily attendance rate of each production line, divide the average daily production volume by the average daily attendance rate to calculate the output evaluation value, and evaluate the output level of each employee based on the output evaluation value. Step 2: Obtain the output level and length of service of different employees to calculate the length of service correlation. There is a preset correlation threshold. When the length of service correlation is less than the correlation threshold, the turnover risk parameters of each production line are calculated based on the employee turnover rate, the number of recruits and the proportion of employees of each output level. When the length of service correlation is greater than or equal to the correlation threshold, the turnover risk parameters are further calculated by combining the proportion of employees of each output level among the departing employees. Step 3: Calculate the production target completion rate of each production line based on the turnover risk parameters, obtain the minimum value of the production target completion rate of all production lines as the theoretical production value, analyze and calculate the human resource risk parameters, and optimize human resources for the production line corresponding to the theoretical production value when the human resource risk parameters are greater than the preset human resource risk threshold. Step 4: Assess product warehousing risks based on product sales and production data, analyze and calculate the warehousing risk index, and adjust the production line output when the warehousing risk index is greater than the preset warehousing risk threshold. Combine human risk parameters to calculate the enterprise's structured risk index. Step 5: Construct an outbound product system consisting of different batches of products, adjust the outbound quantity of each batch of products according to the outbound product system, and optimize the proportion of different batches of products in the outbound product system based on the quality feedback data of different batches of products.

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